Internet of Things (IoT) connects sensing devices to the Internet for the purpose of exchanging information. Location information is one of the most crucial pieces of information required to achieve intelligent and context-aware IoT systems. Recently, positioning and localization functions have been realized in a large amount of IoT systems. However, security and privacy threats related to positioning in IoT have not been sufficiently addressed so far. In this paper, we survey solutions for improving the robustness, security, and privacy of location-based services in IoT systems. First, we provide an in-depth evaluation of the threats and solutions related to both global navigation satellite system (GNSS) and non-GNSS-based solutions. Second, we describe certain cryptographic solutions for security and privacy of positioning and location-based services in IoT. Finally, we discuss the state-of-the-art of policy regulations regarding security of positioning solutions and legal instruments to location data privacy in detail. This survey paper addresses a broad range of security and privacy aspects in IoT-based positioning and localization from both technical and legal points of view and aims to give insight and recommendations for future IoT systems providing more robust, secure, and privacy-preserving location-based services.

Robustness, Security and Privacy in Location-Based Services for Future IoT: A Survey

Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.

https://www.mdpi.com/1424-8220/20/7/2068/pdf

A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping.

Simultaneous Localization and Mapping (SLAM) based on LIDAR and MEMS IMU is a kind of autonomous integrated navigation technology. It can provide attitude, velocity position for a small UAV in an indoor frame during the outage of GNSS. A method of integrating the measurements from a LIDAR and a MEMS IMU was proposed in the paper. LIDAR measurements are a set of ranges and scan angles. The angle rates and accelerations from MEMS IMU are used to drive the simplified strapdown INS equations. The first step of the method is environment features extracting from the measurements of LIDAR and constructing a feature map. Then, the model of errors of LIDAR measurement due to the change of the scan plane during the attitude manoeuver is established and compensated based on aiding information from MEMS INS and the assumption about the structural indoor environment. The relative position parameters derived from environmental features delay matching algorithm and the differences of measurements of LIDAR at adjacent times are used to estimate the error of MEMS INS and MEMS sensors by a Kaiman Filter. A LIDAR/MEMS IMU prototype was designed to verify the practicability of the integrated navigation system of LIDAR and MEMS IMU. Some experiments were carried out in a room and the results demonstrated the potential use of the LIDAR/MEMS IMU integration navigation system.

LIDAR/MEMS IMU integrated navigation (SLAM) method for a small UAV in indoor environments

A Multi-Constellation Software-Defined Receiver (MC-SDR) is designed and implemented to extract the Doppler measurements of Low Earth Orbit (LEO) satellite's downlink signals, such as Orbcomm, Iridium-Next, Globalstar, Starlink, OneWeb, SpaceX, etc. The Doppler positioning methods, as one of the main localization algorithms, need a highly accurate receiver design to track the Doppler as a measurement for Extended Kalman Filter (EKF)-based positioning. In this paper, the designed receiver has been used to acquire and track the Doppler shifts of two different kinds of LEO constellations. The extracted Doppler shifts of one Iridium-Next satellite as a burst-based simplex downlink signal and two Orbcomm satellites as continuous signals are considered. Also, with having the Two-Line Element (TLE) for each satellite, the position, and orbital elements of each satellite are known. Finally, the accuracy of the designed receiver is validated using an EKF-based stationary positioning algorithm with an adaptive measurement matrix. Satellite detection and Doppler tracking results are analyzed for each satellite. The positioning results for a stationary receiver showed an accuracy of about 132 m, which means 72% accuracy advancements compared to single constellation positioning.

https://www.mdpi.com/1424-8220/20/20/5866/pdf

Multi-Constellation Software-Defined Receiver for Doppler Positioning with LEO Satellites

Pedestrian dead reckoning is a common technique applied in indoor inertial navigation systems that is able to provide accurate tracking performance within short distances. Sensor drift is the main bottleneck in extending the system to long-distance and long-term tracking. In this paper, a hybrid system integrating traditional pedestrian dead reckoning based on the use of inertial measurement units, short-range radio frequency systems and particle filter map matching is proposed. The system is a drift-free pedestrian navigation system where position error and sensor drift is regularly corrected and is able to provide long-term accurate and reliable tracking. Moreover, the whole system is implemented on a commercial off-the-shelf smartphone and achieves real-time positioning and tracking performance with satisfactory accuracy.

/pdf/a-hybrid-indoor-localization-and-navigation-system-with-map-3x5z7wdgqe.pdf

A Hybrid Indoor Localization and Navigation System with Map Matching for Pedestrians Using Smartphones

This paper describes non-coherent Deeply Coupled GPS/INS integration in a pedestrian navigation system to improve position accuracy and availability in weak signal conditions. A pedestrian navigation system consists of several sensors to calculate a position of a person to guide for example rescue missions. The system presented in this paper consists of a torso mounted IMU and is used for step detection and step length and heading estimation. Additionally a barometer, magnetometer and a GPS sensor for absolute positioning are used. Since pedestrian navigation systems often are used in challenging environments like urban canyons or indoors, the use of GPS signals is often restricted. We will show that by using a Deeply Coupled GPS/INS integration system, tracking of GPS signals under weak signal conditions is possible and a seamless transition between Indoor and outdoor situations is achieved. By applying the information of a position displacement between two steps from the step length and heading estimation GPS tracking and position accuracy can be increased.

Deeply Coupled GPS/INS integration in pedestrian navigation systems in weak signal conditions

This paper presents a laser-aided navigation system for Micro Aerial Vehicles. It is based on a Kalman filter so that GNSS measurements can be incorporated if available. For GNSS-denied areas, the Kalman filter also processes relative pose measurements extracted from laser data. A novel approach for laser-aided Kalman filter navigation is presented which allows using multiple reference scans simultaneously. Furthermore, an addition to avoid growth of the heading angle error is described. Because this Kalman filter based system is a relative navigation system, its position error grows with time. To avoid such an error growth in GNSS denied environments, the Kalman filter is augmented with loop closure detection. A technique is proposed to represent such information in a pose graph and to calculate an improved navigation solution including an error covariance based on covariance intersection. The successful operation of the presented system is validated in several experiments including real flight data, large loops and an outdoor-indoor-outdoor transition.

Laser-aided navigation with loop closure capabilities for Micro Aerial Vehicles in indoor and urban environments

In this paper a non-coherent deeply coupled GNSS/INS system using a multi constellation software defined GNSS receiver is assessed regarding the advantages of multi constellation (GPS/GALILEO/GLONASS) GNSS/INS integration compared to a GPS only based GPS/INS integrated approach. Both navigation systems are used to evaluate the performance of a moving automotive platform during real world test drives in an urban environment. The navigation solutions are compared to a RTK reference solution. The software GNSS receiver design is presented focusing on processing data from multiple GNSS systems including estimation of clock offsets between different GNSS systems. Several combinations of GNSS systems GPS/GLONASS, GPS/GALILEO, GPS only, GLONASS only and GALILEO only are evaluated.

Markus Langer

Papers

Deeply Coupled GPS/INS integration in pedestrian navigation systems in weak signal conditions

Laser-aided navigation with loop closure capabilities for Micro Aerial Vehicles in indoor and urban environments

Multi GNSS constellation deeply coupled GNSS/INS integration for automotive application using a software defined GNSS receiver

Real Time Implementation of a Non-coherent Deeply Coupled GPS/INS System

Weighting and Mitigation of Multipath Effects in GPS Range Measurements in an Urban Environment for Pedestrian Navigation